One of the main focuses of Bayesian deep learning in computer vision tasks is to accurately capture uncertainty of a model's prediction, which allows the deep learning models to prevent making incorrect predictions when their uncertainties are high. Following the successful applications of uncertainty on semantic segmentation and depth estimation tasks [1]}, [2]}, [3]}, some works propose to utilize uncertainty in the object detection task as well [4]}, [5]}. In particular, Xu et al. [4]} propose to merge overlapping bounding boxes based on the weights of their coordinates obtained from uncertainties (i.e., the larger the uncertainties, the lower the weights) when performing soft-NMS [7]}, which contributes to accurately localizing bounding boxes. Also, CertainNet [5]} measures uncertainties using the distances between predicted features and learnable class centroids (i.e., the larger the distances at inference time, the higher the uncertainties), which is based on the work of deterministic uncertainty quantification (DUQ) [9]}. However, it does not use uncertainty for improving model's prediction, but only calculates uncertainty values of predicted objects. Note that, in contrast to those previous works [4]}, [5]} that use uncertainty either on combining overlapping bounding boxes or on identifying less certain objects, we leverage uncertainties in totally different perspectives. That is we propose to refine representations of uncertain objects by transferring knowledge from certain objects to uncertain ones over the directed graph structure, which may correct initially misclassified objects with high uncertainties.
